Learn to Preserve Personality: Federated Foundation Models in Recommendations
This is a novel paradigm for recommendation systems, aiming to enhance user control and personalization in AI-driven agents.
The paper tackles the tradeoff between generalization and personalization in foundation models by proposing federated foundation models (FFMs) as a structural solution, with recommendation systems serving as a testbed to preserve user personality in a decentralized manner.
A core learning challenge for existed Foundation Models (FM) is striking the tradeoff between generalization with personalization, which is a dilemma that has been highlighted by various parameter-efficient adaptation techniques. Federated foundation models (FFM) provide a structural means to decouple shared knowledge from individual specific adaptations via decentralized processes. Recommendation systems offer a perfect testbed for FFMs, given their reliance on rich implicit feedback reflecting unique user characteristics. This position paper discusses a novel learning paradigm where FFMs not only harness their generalization capabilities but are specifically designed to preserve the integrity of user personality, illustrated thoroughly within the recommendation contexts. We envision future personal agents, powered by personalized adaptive FMs, guiding user decisions on content. Such an architecture promises a user centric, decentralized system where individuals maintain control over their personalized agents.